Unsupervised Variational Posterior Learning for Mosaic-Based Hyperspectral Fusion Imaging.

Snapshot hyperspectral imaging based on a spectral filter array (SFA) enables high-speed acquisition but inherently trades spatial resolution for spectral resolution. Fusing a low-resolution (LR) mosaiced image with a high-resolution (HR) panchromatic (PAN) image offers a promising solution, yet existing methods often exhibit spectral distortion or spatial degradation, especially in unsupervised settings without ground truth. To address this issue, we propose an unsupervised variational posterior learning framework that extends deterministic EI-based fusion into a probabilistic latent-variable formulation. Unlike existing EI-based methods that directly predict a single HR hyperspectral image (HSI) together with a deterministic degradation model, the proposed framework models the HR HSI, spectral response function (SRF), and observation-noise statistics through corresponding posterior distributions. The EI prior is further incorporated as a posterior-consistency constraint between the estimates inferred from original, geometrically transformed, and posterior-predictive observations. Combined with the physical imaging model, this formulation introduces additional stochastic and distribution-level regularization to reduce inference ambiguity without requiring reference HR HSIs. Extensive experiments on both simulated and real-world datasets demonstrate the superiority of the proposed method over the state-of-the-art (SOTA) approaches.